Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
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Genetic Programming and Evolvable Machines
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Engineering Applications of Artificial Intelligence
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Applied Soft Computing
Engineering Applications of Artificial Intelligence
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Computers and Operations Research
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Engineering Applications of Artificial Intelligence
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Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
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IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This article is betrothed to serve as a continuation of the emerging swarm techniques to solve supply chain problems. Our aim is to map some of the pressing research challenges contributed by the artificial intelligence community and to develop an improved algorithm: Co-evolutionary immuno-particle swarm optimisation with penetrated hyper-mutation (COIPSO-PHM). In this paper, we proposed a new algorithm which uses clonal selection approach in particle swarm optimisation by embedding co-evolutionary theory to solve the problem of inventory replenishment in distributed plant-warehouse-retailer system. Constraint handling is explicitly taken care by implanting augmented lagrangian concept. To demonstrate the efficiency of the algorithm, its performance are evaluated and compared on 10 benchmarked problems (made constrained problem via random initialisation in the infeasible zone) including functions with uni-modalities as well as multi-modalities. The result follows shows superior performance of the algorithm in every respect.